IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v7y2015i8p10664-10683d53872.html
   My bibliography  Save this article

Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model

Author

Listed:
  • Ying-Fang Huang

    (Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, Sanmin District, Kaohsiung City 80778, Taiwan)

  • Chia-Nan Wang

    (Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, Sanmin District, Kaohsiung City 80778, Taiwan)

  • Hoang-Sa Dang

    (Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, Sanmin District, Kaohsiung City 80778, Taiwan)

  • Shun-Te Lai

    (Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, Sanmin District, Kaohsiung City 80778, Taiwan)

Abstract

Electronic paper (e-paper) is a major sector of Taiwan’s Optoelectronic industry. It has paid much attention on the development of flexible displays. Even though the market is booming, the future is still unclear for business development. No research has yet forecasted the future market size of the e-paper industry. In addition, proposing an appropriate forecasting model to understand the trend of this industry plays a crucial role for market players and government’s authorities in formulating correct strategies. Therefore, in this paper, the future market size of Taiwan’s e-paper industry is predicted by an effective combined grey model. Two combinations of DGM(2,1) and Verhulst model with Fourier series and Markov chain, namely FM-Verhulst and FMDGM(2,1), were presented. Based on the annual data of Taiwan’s e-paper industry, the results show that the forecasting performances of two FM-Verhulst and FMDGM(2,1) models are highly accurate compared with other grey models. Precision is 96.36% and 97.77%, respectively. However, for long-term prediction, the FMDGM(2,1) model obtains the best performance in all proposed grey models. With obtained forecasting results in Taiwan’s e-paper industry by the FMDGM(2,1) model, it can be pointed out that the future market size of Taiwan’s e-paper would slowly increase in the next few years.

Suggested Citation

  • Ying-Fang Huang & Chia-Nan Wang & Hoang-Sa Dang & Shun-Te Lai, 2015. "Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model," Sustainability, MDPI, vol. 7(8), pages 1-20, August.
  • Handle: RePEc:gam:jsusta:v:7:y:2015:i:8:p:10664-10683:d:53872
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/7/8/10664/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/7/8/10664/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    2. Gholam Hossein Hasantash & Hamidreza Mostafaei & Shaghayegh Kordnoori, 2012. "Modelling the Errors of EIA's Oil Prices and Production Forecasts by the Grey Markov Model," International Journal of Economics and Financial Issues, Econjournals, vol. 2(3), pages 312-319.
    3. Hsiao-Tien Pao & Yao-Yu Chih, 2005. "Comparison of Linear and Nonlinear Models for Panel Data Forecasting: Debt Policy in Taiwan," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 525-541.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hoang-Sa Dang & Thuy-Mai-Trinh Nguyen & Chia-Nan Wang & Jen-Der Day & Thi Minh Han Dang, 2020. "Grey System Theory in the Study of Medical Tourism Industry and Its Economic Impact," IJERPH, MDPI, vol. 17(3), pages 1-23, February.
    2. Hoang-Sa Dang & Ying-Fang Huang & Chia-Nan Wang & Thuy-Mai-Trinh Nguyen, 2016. "An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry," Sustainability, MDPI, vol. 8(10), pages 1-14, October.
    3. Toly Chen, 2016. "Competitive and Sustainable Manufacturing in the Age of Globalization," Sustainability, MDPI, vol. 9(1), pages 1-5, December.
    4. Chia-Nan Wang & Hong-Xuyen Thi Ho & Shih-Hsiung Luo & Tsung-Fu Lin, 2017. "An Integrated Approach to Evaluating and Selecting Green Logistics Providers for Sustainable Development," Sustainability, MDPI, vol. 9(2), pages 1-21, February.
    5. Ping Wang & Bangzhu Zhu, 2016. "Estimating the Contribution of Industry Structure Adjustment to the Carbon Intensity Target: A Case of Guangdong," Sustainability, MDPI, vol. 8(4), pages 1-11, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. V. R. Bityukova, 2022. "Environmental Consequences of the Transformation of the Sectoral Structure of the Economy of Russian Regions and Cities in the Post-Soviet Period," Regional Research of Russia, Springer, vol. 12(1), pages 96-111, March.
    2. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    3. Robert Freeman & Adam Koch & Haidan Li, 2011. "Can historical returns-earnings relations predict price responses to earnings news?," Review of Quantitative Finance and Accounting, Springer, vol. 37(1), pages 35-62, July.
    4. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    5. Suat Ozturk & Feride Ozturk, 2018. "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(2), pages 52-60, February.
    6. Wu, Qunli & Peng, Chenyang, 2017. "A hybrid BAG-SA optimal approach to estimate energy demand of China," Energy, Elsevier, vol. 120(C), pages 985-995.
    7. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    8. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    9. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    10. Fu, Yang & Zheng, Zeyu, 2020. "Volatility modeling and the asymmetric effect for China’s carbon trading pilot market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    11. Ene, Seval & Öztürk, Nursel, 2017. "Grey modelling based forecasting system for return flow of end-of-life vehicles," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 155-166.
    12. Sinha, Avik, 2017. "Examination of oil import-exchange nexus for India after currency crisis," MPRA Paper 100359, University Library of Munich, Germany, revised 2017.
    13. R. Rajesh, 2023. "Grey Markov Models for Predicting the Social Sustainability Performances of Firms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 168(1), pages 297-351, August.
    14. Minglu Ma & Min Su & Shuyu Li & Feng Jiang & Rongrong Li, 2018. "Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    15. Juan Bógalo & Pilar Poncela & Eva Senra, 2021. "Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    16. Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.
    17. Dashti, Reza & Afsharnia, Saeed & Ghaderi, Farid, 2010. "AGA (Asset Governance Assessment) for analyzing affect of subsidy on MC (Marginal Cost) in electricity distribution sector," Energy, Elsevier, vol. 35(12), pages 4996-5007.
    18. Emre Yakut & Ezel Özkan, 2020. "Modeling of Energy Consumption Forecast with Economic Indicators Using Particle Swarm Optimization and Genetic Algorithm: An Application in Turkey between 1979 and 2050," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(1), pages 59-78, June.
    19. Dima, Bogdan & Dima, Ştefana Maria, 2017. "Energy consumption synchronization between Europe, United States and Japan: A spectral analysis assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1261-1271.
    20. Tarek Eldomiaty & Marina Apaydin & Mona Yusuf & Mohamed Rashwan, 2023. "How Do Stock Market Development and Competitiveness Affect Equity Risk Premium? Implications from World Economies," IJFS, MDPI, vol. 11(1), pages 1-19, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:7:y:2015:i:8:p:10664-10683:d:53872. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.